Alejandro Hernandez Arieta

Body Schema in Robotics: A Review

How is our body imprinted in our brain? This seemingly simple question is a subject of investigations of diverse disciplines, psychology, and philosophy originally complemented by neurosciences more recently. Despite substantial efforts, the mysteries of body representations are far from uncovered. The most widely used notions-body image and body schema-are still waiting to be clearly defined. The mechanisms that underlie body representations are coresponsible for the admiring capabilities that humans or many mammals can display: combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These features are also desirable in robots. This paper surveys the body representations in biology from a functional or computational perspective to set ground for a review of the concept of body schema in robotics. First, we examine application-oriented research: how a robot can improve its capabilities by being able to automatically synthesize, extend, or adapt a model of its body. Second, we summarize the research area in which robots are used as tools to verify hypotheses on the mechanisms underlying biological body representations. We identify trends in these research areas and propose future research directions.

Mutual Adaptation in a Prosthetics Application

Prosthetic care for handicapped persons requires new and reliable robotics technology. In this paper, developmental approaches for prosthetic applications are described. In addition, the challenges associated with the adaptation and control of materials for human hand prosthetics are presented. The new technology of robotics for prosthetics provides many possibilities for the detection of human intention. This is particularly true with the use of electromyogram (EMG) and mechanical actuation with multiple degrees of freedom. The EMG signal is a nonlinear wave, and has time dependency and big individual differences. The EMG signal is a nonlinear wave that has time dependency and significant differences from one individual to another. A method for how an individual adapts to the processing of EMG signals is being studied to determine and classify a human’s intention to move. A prosthetic hand with 11 degrees of freedom (DOF) was developed for this study. In order to make it light-weight, an adaptive joint mechanism was applied. The application results demonstrate the challenges for human adaptation. The f-MRI data show a process of replacement from a phantom limb image to a prosthetic hand image.

Study on the Effects of Electrical Stimulation on the Pattern Recognition for an EMG Prosthetic Application

The need of biofeedback in man-machine interfaces is of vital importance for the development of subconscious control with external devices. In order to obtain extended proprioception, in other words, to include the external devices into the body schema, we need to provide with more feedback channels to the human body. In this study we look into the use of electrical stimulation as biofeedback and its effects over the pattern recognition process from the EMG signals that controls the hand movements

FES as Biofeedback for an EMG Controlled Prosthetic Hand

The importance of providing with biofeedback when interacting with man-machine interfaces has to be considered when developing new applications, in particular, with externally powered prosthetic devices. In this study we look to develop a tactile biofeedback system for this purpose using electrical stimulation. In this paper we study the ability of the human body to recognize different patterns of stimulation using two methods: surface and interferential current stimulation and their influence over the EMG acquisition process. The goal is to develop an appropriate feedback source to the human body to be used along with an EMG controlled prosthetic hand.

Multichannel audio biofeedback for dynamical coupling between prosthetic hands and their users

Purpose – It is widely agreed that amputees have to rely on visual input to monitor and control the position of the prosthesis while reaching and grasping because of the lack of proprioceptive feedback. Therefore, visual information has been a prerequisite for prosthetic hand biofeedback studies. This is why, the underlying characteristics of other artificial feedback methods used to this day, such as auditive, electro‐tactile, or vibro‐tactile feedback, has not been clearly explored. The purpose of this paper is to explore whether it is possible to use audio feedback alone to convey more than one independent variable (multichannel) simultaneously, without relying on the vision, to improve the learning of a new perceptions, in this case, to learn and understand the artificial proprioception of a prosthetic hand while reaching.Design/methodology/approach – Experiments are conducted to determine whether the audio signals could be used as a multi‐variable dynamical sensory substitution in reaching movements ...

Gesture recognition in upper-limb prosthetics: A viability study using dynamic time warping and gyroscopes

One of the significant challenges in the upper-limb-prosthetics research field is to identify appropriate interfaces that utilize the full potential of current state-of-the-art neuroprostheses. As the new generation of such prostheses paces towards approximating the human physiological performance in terms of movement dexterity and sensory feedback, it is clear that current non-invasive interfaces are still severely limited. Surface electromyography, the interface ubiquitously used in the field, is riddled with several shortcomings. Gesture recognition, an interface pervasively used in wearables and mobile devices, shows a strong potential as a non-invasive upper-limb prosthetic interface. This study aims at showcasing its potential in the field by using gyroscope sensors. To this end, we (1) explore the viability of Dynamic Time Warping as a classification method for upper-limb prosthetics and (2) look for appropriate sensor locations on the body. Results indicate an optimal classification rate of 97.53%, σ = 8.74 using a sensor located proximal to the endpoint performing a gesture.

Design and evaluation of a multi-modal haptic skin stimulation apparatus

Human grasping and manipulation are facilitated by cutaneous mechanoreceptors that provide information about contact location, pressure, and events such as making and breaking contact. A challenge in designing haptic feedback devices for the wearer of a prosthetic hand is simultaneous display of multiple types of haptic information. We present the preliminary design and evaluation of an apparatus for relaying multi-modal haptic information. The apparatus moves a set of contact points tangentially over the skin at a controlled speed, with controlled normal force. We apply this stimulus to an artificial skin instrumented with an embedded accelerometer, and characterize the resulting signals. Vibration frequency increases with applied normal force and tangential speed, whereas vibration amplitude increases with normal force and depends on skin properties. The results indicate that different forces and speeds can, under some conditions, be discriminated using vibration signals alone. Accurate identification of speeds is provided by series of vibration events that depend on the spatial distribution of contact points. This study motivates future work to perform human perception studies and create a wearable haptic display for prosthetics based on this concept.

A fMRI study of the cross-modal interaction in the brain with an adaptable EMG prosthetic hand with biofeedback.

Mutual adaptation between man and machine is necessary for the development of more efficient devices that allows easy adaptation. The interaction with intelligent machines involves adaptation processes from both the user and the machine. The human body has the ability to change its body schema to include external tools in it, using this fact we proposed the design of intelligent machines with biofeedback to the user, permitting in this way, development of subconscious control of external devices. We propose the case of an EMG prosthetic hand with biofeedback to study the adaptation process between man-machine. Our system includes an EMG classification system to acquire the intended movement from the user. We use electrical stimulation as a provider of tactile feedback, to interact with the human body. We use a functional Magnetic Resonance Image study while using the prosthetic device while receiving biofeedback to measure the activation levels in the amputees brain

Effects of sensory augmentation on postural control and gait symmetry of transfemoral amputees: a case description.

AbstractDespite recent advances in leg prosthetics, transfemoral amputees still experience limitations in postural control and gait symmetry. It has been hypothesized that artificial sensory information might improve the integration of the prosthesis into the human sensory-motor control loops and, thus, reduce these limitations. In three transfemoral amputees, we investigated the effect of Electrotactile Moving Sensation for Sensory Augmentation (EMSSA) without training and present preliminary findings. Experimental conditions included standing with open/closed eyes on stable/unstable ground as well as treadmill walking. For standing conditions, spatiotemporal posturographic measures and sample entropy were derived from the center of pressure. For walking conditions, step length and stance duration were calculated. Conditions without feedback showed effects congruent with findings in the literature, e.g., asymmetric weight bearing and step length, and validated the collected data. During standing, with EMSSA a tendency to influence postural control in a negative way was found: Postural control was less effective and less efficient and the prosthetic leg was less involved. Sample entropy tended to decrease, suggesting that EMSSA demanded increased attention. During walking, with EMSSA no persistent positive effect was found. This contrasts the positive subjective assessment and the positive effect on one subject’s step length.

Contact mechanics for soft robotic fingers: modeling and experimentation

Human fingers possess mechanical characteristics, which enable them to manipulate objects. In robotics, the study of soft fingertip materials for manipulation has been going on for a while; however, almost all previous researches have been carried on hemispherical shapes whereas this study concentrates on the use of hemicylindrical shapes. These shapes were found to be more resistant to elastic deformations for the same materials. The purpose of this work is to generate a modified nonlinear contact-mechanics theory for modeling soft fingertips, which is proposed as a power-law equation. The contact area of a hemicylindrical soft fingertip is proportional to the normal force raised to the power of γ cy , which ranges from 0 to 1/2. Subsuming the Timoshenko and Goodier (S. P. Timoshenko and J. N. Goodier, Theory of Elasticity , 3rd ed. (McGraw-Hill, New York, 1970) pp. 414–420) linear contact theory for cylinders confirms the proposed power equation. We applied a weighted least-squares curve fitting to analyze the experimental data for different types of silicone (RTV 23, RTV 1701, and RTV 240). Our experimental results supported the proposed theoretical prediction. Results for human fingers and hemispherical soft fingers were also compared.